Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Deng, Zhongying"'
Autor:
Chen, Pengcheng, Ye, Jin, Wang, Guoan, Li, Yanjun, Deng, Zhongying, Li, Wei, Li, Tianbin, Duan, Haodong, Huang, Ziyan, Su, Yanzhou, Wang, Benyou, Zhang, Shaoting, Fu, Bin, Cai, Jianfei, Zhuang, Bohan, Seibel, Eric J, He, Junjun, Qiao, Yu
Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals, and can be applied in various fields. In the medical field, LVLMs have a high potential to offer substantial assistance
Externí odkaz:
http://arxiv.org/abs/2408.03361
Autor:
Aviles-Rivero, Angelica I., Cheng, Chun-Wun, Deng, Zhongying, Kourtzi, Zoe, Schönlieb, Carola-Bibiane
Early detection of Alzheimer's disease's precursor stages is imperative for significantly enhancing patient outcomes and quality of life. This challenge is tackled through a semi-supervised multi-modal diagnosis framework. In particular, we introduce
Externí odkaz:
http://arxiv.org/abs/2403.12719
Autor:
Bryutkin, Andrey, Huang, Jiahao, Deng, Zhongying, Yang, Guang, Schönlieb, Carola-Bibiane, Aviles-Rivero, Angelica
Publikováno v:
Proceedings of Machine Learning Research, Vol. 235, pp. 4624-4641, 2024
We present a novel graph transformer framework, HAMLET, designed to address the challenges in solving partial differential equations (PDEs) using neural networks. The framework uses graph transformers with modular input encoders to directly incorpora
Externí odkaz:
http://arxiv.org/abs/2402.03541
Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies
Autor:
Chen, Pengcheng, Huang, Ziyan, Deng, Zhongying, Li, Tianbin, Su, Yanzhou, Wang, Haoyu, Ye, Jin, Qiao, Yu, He, Junjun
OpenAI's latest large vision-language model (LVLM), GPT-4V(ision), has piqued considerable interest for its potential in medical applications. Despite its promise, recent studies and internal reviews highlight its underperformance in specialized medi
Externí odkaz:
http://arxiv.org/abs/2312.04344
Autor:
Ye, Jin, Cheng, Junlong, Chen, Jianpin, Deng, Zhongying, Li, Tianbin, Wang, Haoyu, Su, Yanzhou, Huang, Ziyan, Chen, Jilong, Jiang, Lei, Sun, Hui, Zhu, Min, Zhang, Shaoting, He, Junjun, Qiao, Yu
Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes. Its success largely owes to massive labeled training data. However, directly applying SAM to medical ima
Externí odkaz:
http://arxiv.org/abs/2311.11969
Autor:
Wang, Haoyu, Guo, Sizheng, Ye, Jin, Deng, Zhongying, Cheng, Junlong, Li, Tianbin, Chen, Jianpin, Su, Yanzhou, Huang, Ziyan, Shen, Yiqing, Fu, Bin, Zhang, Shaoting, He, Junjun, Qiao, Yu
Existing volumetric medical image segmentation models are typically task-specific, excelling at specific target but struggling to generalize across anatomical structures or modalities. This limitation restricts their broader clinical use. In this pap
Externí odkaz:
http://arxiv.org/abs/2310.15161
Autor:
Huang, Ziyan, Deng, Zhongying, Ye, Jin, Wang, Haoyu, Su, Yanzhou, Li, Tianbin, Sun, Hui, Cheng, Junlong, Chen, Jianpin, He, Junjun, Gu, Yun, Zhang, Shaoting, Gu, Lixu, Qiao, Yu
Although deep learning have revolutionized abdominal multi-organ segmentation, models often struggle with generalization due to training on small, specific datasets. With the recent emergence of large-scale datasets, some important questions arise: \
Externí odkaz:
http://arxiv.org/abs/2309.03906
Autor:
Cheng, Junlong, Ye, Jin, Deng, Zhongying, Chen, Jianpin, Li, Tianbin, Wang, Haoyu, Su, Yanzhou, Huang, Ziyan, Chen, Jilong, Jiang, Lei, Sun, Hui, He, Junjun, Zhang, Shaoting, Zhu, Min, Qiao, Yu
The Segment Anything Model (SAM) represents a state-of-the-art research advancement in natural image segmentation, achieving impressive results with input prompts such as points and bounding boxes. However, our evaluation and recent research indicate
Externí odkaz:
http://arxiv.org/abs/2308.16184
Autor:
Huang, Ziyan, Wang, Haoyu, Deng, Zhongying, Ye, Jin, Su, Yanzhou, Sun, Hui, He, Junjun, Gu, Yun, Gu, Lixu, Zhang, Shaoting, Qiao, Yu
Large-scale models pre-trained on large-scale datasets have profoundly advanced the development of deep learning. However, the state-of-the-art models for medical image segmentation are still small-scale, with their parameters only in the tens of mil
Externí odkaz:
http://arxiv.org/abs/2304.06716
Target domain pseudo-labelling has shown effectiveness in unsupervised domain adaptation (UDA). However, pseudo-labels of unlabeled target domain data are inevitably noisy due to the distribution shift between source and target domains. This paper pr
Externí odkaz:
http://arxiv.org/abs/2303.05734